{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/slurm_data/youcheng.li/anaconda3/envs/ddpm/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import tonic\n",
    "\n",
    "dataset = tonic.datasets.NMNIST(save_to='./data', train=True)\n",
    "events, target = dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tonic.utils.plot_event_grid(events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tonic.transforms as transforms\n",
    "\n",
    "sensor_size = tonic.datasets.NMNIST.sensor_size\n",
    "\n",
    "# Denoise removes isolated, one-off events\n",
    "# time_window\n",
    "frame_transform = transforms.Compose([transforms.Denoise(filter_time=10000),\n",
    "                                      transforms.ToFrame(sensor_size=sensor_size,\n",
    "                                                         time_window=1000)\n",
    "                                     ])\n",
    "\n",
    "trainset = tonic.datasets.NMNIST(save_to='./data', transform=frame_transform, train=True)\n",
    "testset = tonic.datasets.NMNIST(save_to='./data', transform=frame_transform, train=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from tonic import DiskCachedDataset\n",
    "\n",
    "\n",
    "cached_trainset = DiskCachedDataset(trainset, cache_path='./cache/nmnist/train')\n",
    "cached_dataloader = DataLoader(cached_trainset)\n",
    "\n",
    "batch_size = 128\n",
    "from torch.utils.data import DataLoader, SubsetRandomSampler\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_sample_batched():\n",
    "    events, target = next(iter(cached_dataloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "\n",
    "transform = tonic.transforms.Compose([torch.from_numpy,\n",
    "                                      torchvision.transforms.RandomRotation([-10,10])])\n",
    "\n",
    "cached_trainset = DiskCachedDataset(trainset, transform=transform, cache_path='./cache/nmnist/train')\n",
    "\n",
    "# no augmentations for the testset\n",
    "cached_testset = DiskCachedDataset(testset, cache_path='./cache/nmnist/test')\n",
    "\n",
    "batch_size = 1\n",
    "# trainloader = DataLoader(cached_trainset, batch_size=batch_size, collate_fn=tonic.collation.PadTensors(batch_first=False), shuffle=True)\n",
    "\n",
    "dataset_length = len(cached_trainset)\n",
    "indices = np.arange(0, dataset_length, 600)\n",
    "sampler = SubsetRandomSampler(indices)\n",
    "\n",
    "trainloader = DataLoader(cached_trainset, batch_size=batch_size, collate_fn=tonic.collation.PadTensors(batch_first=False), shuffle=False, sampler=sampler)\n",
    "testloader = DataLoader(cached_testset, batch_size=batch_size, collate_fn=tonic.collation.PadTensors(batch_first=False))\n",
    "\n",
    "print(len(trainloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import snntorch as snn\n",
    "from snntorch import surrogate\n",
    "from snntorch import functional as SF\n",
    "from snntorch import spikeplot as splt\n",
    "from snntorch import utils\n",
    "import torch.nn as nn\n",
    "import torch\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"mps\") if torch.backends.mps.is_available() else torch.device(\"cpu\")\n",
    "\n",
    "# neuron and simulation parameters\n",
    "spike_grad = surrogate.atan()\n",
    "beta = 0.5\n",
    "\n",
    "#  Initialize Network\n",
    "net = nn.Sequential(nn.Conv2d(2, 12, 5),\n",
    "                    nn.MaxPool2d(2),\n",
    "                    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),\n",
    "                    nn.Conv2d(12, 32, 5),\n",
    "                    nn.MaxPool2d(2),\n",
    "                    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),\n",
    "                    nn.Flatten(),\n",
    "                    nn.Linear(32*5*5, 10),\n",
    "                    snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True)\n",
    "                    ).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this time, we won't return membrane as we don't need it\n",
    "\n",
    "def forward_pass(net, data):\n",
    "  spk_rec = []\n",
    "  utils.reset(net)  # resets hidden states for all LIF neurons in net\n",
    "\n",
    "  for step in range(data.size(0)):  # data.size(0) = number of time steps\n",
    "      spk_out, mem_out = net(data[step])\n",
    "      spk_rec.append(spk_out)\n",
    "\n",
    "  return torch.stack(spk_rec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(net.parameters(), lr=2e-2, betas=(0.9, 0.999))\n",
    "loss_fn = SF.mse_count_loss(correct_rate=0.8, incorrect_rate=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100it [00:54,  1.83it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 59.00%\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import os\n",
    "num_epochs = 1\n",
    "num_iters = 10\n",
    "\n",
    "loss_hist = []\n",
    "acc_hist = []\n",
    "\n",
    "use_pretrained = True\n",
    "\n",
    "if os.path.exists('./model/nmnist.pth') and use_pretrained:\n",
    "  net.load_state_dict(torch.load('./model/nmnist.pth'))\n",
    "  print('Model loaded')\n",
    "  spk_list = []\n",
    "  target_list = []\n",
    "  for i, (data, targets) in tqdm(enumerate(iter(trainloader))):\n",
    "      data = data.to(device)\n",
    "      targets = targets.to(device)\n",
    "\n",
    "      net.eval()\n",
    "      spk_rec = forward_pass(net, data)\n",
    "      loss_val = loss_fn(spk_rec, targets)\n",
    "\n",
    "      # Store loss history for future plotting\n",
    "      loss_hist.append(loss_val.item())\n",
    "\n",
    "      # print(f\"Epoch {epoch}, Iteration {i} \\nTrain Loss: {loss_val.item():.2f}\")\n",
    "\n",
    "      # acc = SF.accuracy_rate(spk_rec, targets)\n",
    "      _, idx = spk_rec.sum(dim=0).max(1)\n",
    "      spk_list.extend(idx)\n",
    "      target_list.extend(targets)\n",
    "      # if i == num_iters:\n",
    "      #   break\n",
    "  spk_list = torch.stack(spk_list).cpu().numpy()\n",
    "  target_list = torch.stack(target_list).cpu().numpy()\n",
    "  accuracy = np.mean(spk_list == target_list)\n",
    "  acc_hist.append(accuracy)\n",
    "  # print(f\"Accuracy: {acc * 100:.2f}%\\n\")\n",
    "  tqdm.write(f\"Accuracy: {accuracy * 100:.2f}%\\n\")\n",
    "else:\n",
    "  # training loop\n",
    "  for epoch in range(num_epochs):\n",
    "      tqdm.write(f\"Epoch {epoch + 1}\\n-------------------------------\")\n",
    "      spk_list = []\n",
    "      target_list = []\n",
    "      for i, (data, targets) in tqdm(enumerate(iter(trainloader))):\n",
    "          data = data.to(device)\n",
    "          targets = targets.to(device)\n",
    "\n",
    "          net.train()\n",
    "          spk_rec = forward_pass(net, data)\n",
    "          loss_val = loss_fn(spk_rec, targets)\n",
    "\n",
    "          # Gradient calculation + weight update\n",
    "          optimizer.zero_grad()\n",
    "          loss_val.backward()\n",
    "          optimizer.step()\n",
    "\n",
    "          # Store loss history for future plotting\n",
    "          loss_hist.append(loss_val.item())\n",
    "\n",
    "          # print(f\"Epoch {epoch}, Iteration {i} \\nTrain Loss: {loss_val.item():.2f}\")\n",
    "\n",
    "          # acc = SF.accuracy_rate(spk_rec, targets)\n",
    "          _, idx = spk_rec.sum(dim=0).max(1)\n",
    "          spk_list.extend(idx)\n",
    "          target_list.extend(targets)\n",
    "          # if i == num_iters:\n",
    "          #   break\n",
    "      spk_list = torch.stack(spk_list).cpu().numpy()\n",
    "      target_list = torch.stack(target_list).cpu().numpy()\n",
    "      accuracy = np.mean(spk_list == target_list)\n",
    "      acc_hist.append(accuracy)\n",
    "      # print(f\"Accuracy: {acc * 100:.2f}%\\n\")\n",
    "      tqdm.write(f\"Accuracy: {accuracy * 100:.2f}%\\n\")\n",
    "          # training loop breaks after 50 iterations\n",
    "          # if i == num_iters:\n",
    "          #   break\n",
    "\n",
    "torch.save(net.state_dict(), './model/nmnist.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.59\n",
      "Accuracy for class 0: 0.9\n",
      "Accuracy for class 1: 1.0\n",
      "Accuracy for class 2: 0.7\n",
      "Accuracy for class 3: 0.3\n",
      "Accuracy for class 4: 1.0\n",
      "Accuracy for class 5: 0.1\n",
      "Accuracy for class 6: 0.7777777777777778\n",
      "Accuracy for class 7: 0.5454545454545454\n",
      "Accuracy for class 8: 0.2\n",
      "Accuracy for class 9: 0.3333333333333333\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# acc per class\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pred = spk_list\n",
    "target = target_list\n",
    "\n",
    "cm = confusion_matrix(target, pred)\n",
    "acc = accuracy_score(target, pred)\n",
    "\n",
    "print(f'Accuracy: {acc}')\n",
    "\n",
    "for i in range(10):\n",
    "  print(f'Accuracy for class {i}: {cm[i,i]/cm[i,:].sum()}')\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title(f'Accuracy: {acc}')\n",
    "plt.savefig('./figures/nmnist_confusion_matrix.png')\n",
    "# plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot Loss\n",
    "fig = plt.figure(facecolor=\"w\")\n",
    "plt.plot(acc_hist)\n",
    "plt.title(\"Train Set Accuracy\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/100 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The target label is: 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|          | 1/100 [00:00<00:49,  1.99it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The target label is: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 2/100 [00:00<00:48,  2.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The target label is: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  3%|▎         | 3/100 [00:01<00:47,  2.04it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The target label is: 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4%|▍         | 4/100 [00:01<00:47,  2.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The target label is: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|▌         | 5/100 [00:02<00:47,  2.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The target label is: 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|▋         | 7/100 [00:03<00:35,  2.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The target label is: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 19%|█▉        | 19/100 [00:03<00:07, 10.51it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The target label is: 8\n",
      "The target label is: 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22%|██▏       | 22/100 [00:04<00:12,  6.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The target label is: 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22%|██▏       | 22/100 [00:05<00:18,  4.23it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAAASCHAAAABIIMABAAAggQAHAACABAIcAAAAEghwAAAASCDAAQAAIIEABwAAgAQCHAAAABIIcAAAAEggwAEAACCBAAcAAIAEAhwAAAASCHAAAABIIMABAAAggQAHAACABAIcAAAAEghwAAAASCDAAQAAIIEABwAAgAQCHAAAABIIcAAAAEggwAEAACCBAAcAAIAEAhwAAAASCHAAAABIIMABAAAggQAHAACABAIcAAAAEghwAAAASCDAAQAAIIEABwAAgAQCHAAAABIIcAAAAEggwAEAACCBAAcAAIAEAhwAAAASCHAAAABIIMABAAAggQAHAACABAIcAAAAEghwAAAASCDAAQAAIIEABwAAgAQCHAAAABIIcAAAAEggwAEAACCBAAcAAIAEAhwAAAASCHAAAABIIMABAAAggQAHAACABAIcAAAAEghwAAAASCDAAQAAIIEABwAAgAQCHAAAABIIcAAAAEggwAEAACCBAAcAAIAEAhwAAAASCHAAAABIIMABAAAgwagCfO3atTFjxoyoqamJxsbG2LZt2ymt27BhQ1RUVMT8+fNHc1kAAAAYt8oO8I0bN0Zra2u0tbXFjh07YubMmdHS0hKHDh16x3X79++Pr3zlK3HDDTeMerMAAAAwXpUd4I899lh84QtfiKVLl8ZHP/rRWLduXVxwwQXx3e9+96RrBgYG4vOf/3w89NBDcemll57WhgEAAGA8KivA+/v7Y/v27dHc3PzHJ6isjObm5ujs7Dzpuq9//esxefLkuPXWW0e/UwAAABjHJpQz+ciRIzEwMBD19fXDxuvr62PPnj0jrvnZz34WTz31VOzateuUr3Ps2LE4duzY0M+9vb3lbBMAAADOOWP6KehHjx6NRYsWxfr162PSpEmnvK69vT3q6uqGHg0NDWO4SwAAABh7Zd0BnzRpUlRVVUV3d/ew8e7u7pgyZcoJ83/xi1/E/v37Y968eUNjg4ODv7/whAmxd+/euOyyy05Yt2LFimhtbR36ube3V4QDAAAwrpUV4NXV1TF79uzo6OgY+iqxwcHB6OjoiC9/+csnzL/iiivilVdeGTb2wAMPxNGjR+Of//mfTxrVpVIpSqVSOVsDAACAc1pZAR4R0draGkuWLIk5c+bE3LlzY82aNdHX1xdLly6NiIjFixfH9OnTo729PWpqauLKK68ctv6iiy6KiDhhHAAAAN7Lyg7wBQsWxOHDh2PlypXR1dUVs2bNii1btgx9MNuBAweisnJMf7UcAAAAxp2KoiiKs72Jd9Pb2xt1dXXR09MTtbW1Z3s7AAAAvMeNRYe6VQ0AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJRhXga9eujRkzZkRNTU00NjbGtm3bTjp3/fr1ccMNN8TEiRNj4sSJ0dzc/I7zAQAA4L2o7ADfuHFjtLa2RltbW+zYsSNmzpwZLS0tcejQoRHnb926NRYuXBgvvfRSdHZ2RkNDQ9x0003xxhtvnPbmAQAAYLyoKIqiKGdBY2NjXHvttfH4449HRMTg4GA0NDTEnXfeGcuXL3/X9QMDAzFx4sR4/PHHY/Hixad0zd7e3qirq4uenp6ora0tZ7sAAABQtrHo0LLugPf398f27dujubn5j09QWRnNzc3R2dl5Ss/x1ltvxdtvvx0XX3zxSeccO3Ysent7hz0AAABgPCsrwI8cORIDAwNRX18/bLy+vj66urpO6TnuvffemDZt2rCI/1Pt7e1RV1c39GhoaChnmwAAAHDOSf0U9NWrV8eGDRviueeei5qampPOW7FiRfT09Aw9Dh48mLhLAAAAOPMmlDN50qRJUVVVFd3d3cPGu7u7Y8qUKe+49pFHHonVq1fHT37yk7j66qvfcW6pVIpSqVTO1gAAAOCcVtYd8Orq6pg9e3Z0dHQMjQ0ODkZHR0c0NTWddN23vvWtePjhh2PLli0xZ86c0e8WAAAAxqmy7oBHRLS2tsaSJUtizpw5MXfu3FizZk309fXF0qVLIyJi8eLFMX369Ghvb4+IiH/8x3+MlStXxjPPPBMzZswY+l3xD3zgA/GBD3zgDL4UAAAAOHeVHeALFiyIw4cPx8qVK6OrqytmzZoVW7ZsGfpgtgMHDkRl5R9vrH/729+O/v7++Nu//dthz9PW1hZf+9rXTm/3AAAAME6U/T3gZ4PvAQcAACDTWf8ecAAAAGB0BDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJRhXga9eujRkzZkRNTU00NjbGtm3b3nH+D37wg7jiiiuipqYmrrrqqti8efOoNgsAAADjVdkBvnHjxmhtbY22trbYsWNHzJw5M1paWuLQoUMjzn/55Zdj4cKFceutt8bOnTtj/vz5MX/+/Hj11VdPe/MAAAAwXlQURVGUs6CxsTGuvfbaePzxxyMiYnBwMBoaGuLOO++M5cuXnzB/wYIF0dfXFz/+8Y+Hxv76r/86Zs2aFevWrTula/b29kZdXV309PREbW1tOdsFAACAso1Fh04oZ3J/f39s3749VqxYMTRWWVkZzc3N0dnZOeKazs7OaG1tHTbW0tISzz///Emvc+zYsTh27NjQzz09PRHx+/8DAAAAYKz9oT/LvGf9jsoK8CNHjsTAwEDU19cPG6+vr489e/aMuKarq2vE+V1dXSe9Tnt7ezz00EMnjDc0NJSzXQAAADgt//M//xN1dXVn5LnKCvAsK1asGHbX/M0334wPfvCDceDAgTP2wuFc09vbGw0NDXHw4EG/asF7lnPO+4FzzvuBc877QU9PT1xyySVx8cUXn7HnLCvAJ02aFFVVVdHd3T1svLu7O6ZMmTLimilTppQ1PyKiVCpFqVQ6Ybyurs4fcN7zamtrnXPe85xz3g+cc94PnHPeDyorz9y3d5f1TNXV1TF79uzo6OgYGhscHIyOjo5oamoacU1TU9Ow+RERL7744knnAwAAwHtR2W9Bb21tjSVLlsScOXNi7ty5sWbNmujr64ulS5dGRMTixYtj+vTp0d7eHhERd911V9x4443x6KOPxs033xwbNmyIn//85/Hkk0+e2VcCAAAA57CyA3zBggVx+PDhWLlyZXR1dcWsWbNiy5YtQx+0duDAgWG36K+77rp45pln4oEHHoj77rsv/uqv/iqef/75uPLKK0/5mqVSKdra2kZ8Wzq8VzjnvB8457wfOOe8HzjnvB+MxTkv+3vAAQAAgPKdud8mBwAAAE5KgAMAAEACAQ4AAAAJBDgAAAAkOGcCfO3atTFjxoyoqamJxsbG2LZt2zvO/8EPfhBXXHFF1NTUxFVXXRWbN29O2imMXjnnfP369XHDDTfExIkTY+LEidHc3Pyufy7gXFDu3+d/sGHDhqioqIj58+eP7QbhDCj3nL/55puxbNmymDp1apRKpbj88sv9twvnvHLP+Zo1a+LDH/5wnH/++dHQ0BD33HNP/O53v0vaLZTnpz/9acybNy+mTZsWFRUV8fzzz7/rmq1bt8bHP/7xKJVK8aEPfSiefvrpsq97TgT4xo0bo7W1Ndra2mLHjh0xc+bMaGlpiUOHDo04/+WXX46FCxfGrbfeGjt37oz58+fH/Pnz49VXX03eOZy6cs/51q1bY+HChfHSSy9FZ2dnNDQ0xE033RRvvPFG8s7h1JV7zv9g//798ZWvfCVuuOGGpJ3C6JV7zvv7++NTn/pU7N+/P5599tnYu3dvrF+/PqZPn568czh15Z7zZ555JpYvXx5tbW2xe/fueOqpp2Ljxo1x3333Je8cTk1fX1/MnDkz1q5de0rzf/nLX8bNN98cn/zkJ2PXrl1x9913x2233RYvvPBCeRcuzgFz584tli1bNvTzwMBAMW3atKK9vX3E+Z/73OeKm2++edhYY2Nj8fd///djuk84HeWe8z91/Pjx4sILLyy+973vjdUW4bSN5pwfP368uO6664rvfOc7xZIlS4rPfvazCTuF0Sv3nH/7298uLr300qK/vz9ri3Dayj3ny5YtK/7mb/5m2Fhra2tx/fXXj+k+4UyIiOK55557xzlf/epXi4997GPDxhYsWFC0tLSUda2zfge8v78/tm/fHs3NzUNjlZWV0dzcHJ2dnSOu6ezsHDY/IqKlpeWk8+FsG805/1NvvfVWvP3223HxxReP1TbhtIz2nH/961+PyZMnx6233pqxTTgtoznnP/rRj6KpqSmWLVsW9fX1ceWVV8aqVatiYGAga9tQltGc8+uuuy62b98+9Db1ffv2xebNm+PTn/50yp5hrJ2pBp1wJjc1GkeOHImBgYGor68fNl5fXx979uwZcU1XV9eI87u6usZsn3A6RnPO/9S9994b06ZNO+EPPpwrRnPOf/azn8VTTz0Vu3btStghnL7RnPN9+/bFf/zHf8TnP//52Lx5c7z++uvxpS99Kd5+++1oa2vL2DaUZTTn/JZbbokjR47EJz7xiSiKIo4fPx533HGHt6DznnGyBu3t7Y3f/va3cf7555/S85z1O+DAu1u9enVs2LAhnnvuuaipqTnb24Ez4ujRo7Fo0aJYv359TJo06WxvB8bM4OBgTJ48OZ588smYPXt2LFiwIO6///5Yt27d2d4anDFbt26NVatWxRNPPBE7duyIH/7wh7Fp06Z4+OGHz/bW4Jxy1u+AT5o0KaqqqqK7u3vYeHd3d0yZMmXENVOmTClrPpxtoznnf/DII4/E6tWr4yc/+UlcffXVY7lNOC3lnvNf/OIXsX///pg3b97Q2ODgYERETJgwIfbu3RuXXXbZ2G4ayjSav8+nTp0a5513XlRVVQ2NfeQjH4murq7o7++P6urqMd0zlGs05/zBBx+MRYsWxW233RYREVdddVX09fXF7bffHvfff39UVrrvx/h2sgatra095bvfEefAHfDq6uqYPXt2dHR0DI0NDg5GR0dHNDU1jbimqalp2PyIiBdffPGk8+FsG805j4j41re+FQ8//HBs2bIl5syZk7FVGLVyz/kVV1wRr7zySuzatWvo8ZnPfGbo00UbGhoytw+nZDR/n19//fXx+uuvD/0DU0TEa6+9FlOnThXfnJNGc87feuutEyL7D//o9PvPuILx7Yw1aHmfDzc2NmzYUJRKpeLpp58u/vu//7u4/fbbi4suuqjo6uoqiqIoFi1aVCxfvnxo/n/+538WEyZMKB555JFi9+7dRVtbW3HeeecVr7zyytl6CfCuyj3nq1evLqqrq4tnn322+PWvfz30OHr06Nl6CfCuyj3nf8qnoDMelHvODxw4UFx44YXFl7/85WLv3r3Fj3/842Ly5MnFN77xjbP1EuBdlXvO29raigsvvLD4t3/7t2Lfvn3Fv//7vxeXXXZZ8bnPfe5svQR4R0ePHi127txZ7Ny5s4iI4rHHHit27txZ/OpXvyqKoiiWL19eLFq0aGj+vn37igsuuKD4h3/4h2L37t3F2rVri6qqqmLLli1lXfecCPCiKIp/+Zd/KS655JKiurq6mDt3bvFf//VfQ//bjTfeWCxZsmTY/O9///vF5ZdfXlRXVxcf+9jHik2bNiXvGMpXzjn/4Ac/WETECY+2trb8jUMZyv37/P8nwBkvyj3nL7/8ctHY2FiUSqXi0ksvLb75zW8Wx48fT941lKecc/72228XX/va14rLLrusqKmpKRoaGoovfelLxf/+7//mbxxOwUsvvTTif2v/4VwvWbKkuPHGG09YM2vWrKK6urq49NJLi3/9138t+7oVReE9IQAAADDWzvrvgAMAAMD7gQAHAACABAIcAAAAEghwAAAASCDAAQAAIIEABwAAgAQCHAAAABIIcAAAAEggwAEAACCBAAcAAIAEAhwAAAASCHAAAABI8P8AhWws06D2oEcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAAASCHAAAABIIMABAAAggQAHAACABAIcAAAAEghwAAAASCDAAQAAIIEABwAAgAQCHAAAABIIcAAAAEggwAEAACCBAAcAAIAEAhwAAAASCHAAAABIIMABAAAggQAHAACABAIcAAAAEghwAAAASCDAAQAAIIEABwAAgAQCHAAAABIIcAAAAEggwAEAACCBAAcAAIAEAhwAAAASCHAAAABIIMABAAAggQAHAACABAIcAAAAEghwAAAASCDAAQAAIIEABwAAgAQCHAAAABIIcAAAAEggwAEAACCBAAcAAIAEAhwAAAASCHAAAABIIMABAAAggQAHAACABAIcAAAAEghwAAAASCDAAQAAIIEABwAAgAQCHAAAABIIcAAAAEggwAEAACCBAAcAAIAEAhwAAAASCHAAAABIIMABAAAggQAHAACABAIcAAAAEghwAAAASCDAAQAAIIEABwAAgAQCHAAAABIIcAAAAEggwAEAACCBAAcAAIAEAhwAAAASCHAAAABIIMABAAAgwagCfO3atTFjxoyoqamJxsbG2LZt2ymt27BhQ1RUVMT8+fNHc1kAAAAYt8oO8I0bN0Zra2u0tbXFjh07YubMmdHS0hKHDh16x3X79++Pr3zlK3HDDTeMerMAAAAwXpUd4I899lh84QtfiKVLl8ZHP/rRWLduXVxwwQXx3e9+96RrBgYG4vOf/3w89NBDcemll57WhgEAAGA8KivA+/v7Y/v27dHc3PzHJ6isjObm5ujs7Dzpuq9//esxefLkuPXWW0e/UwAAABjHJpQz+ciRIzEwMBD19fXDxuvr62PPnj0jrvnZz34WTz31VOzateuUr3Ps2LE4duzY0M+9vb3lbBMAAADOOWP6KehHjx6NRYsWxfr162PSpEmnvK69vT3q6uqGHg0NDWO4SwAAABh7Zd0BnzRpUlRVVUV3d/ew8e7u7pgyZcoJ83/xi1/E/v37Y968eUNjg4ODv7/whAmxd+/euOyyy05Yt2LFimhtbR36ube3V4QDAAAwrpUV4NXV1TF79uzo6OgY+iqxwcHB6OjoiC9/+csnzL/iiivilVdeGTb2wAMPxNGjR+Of//mfTxrVpVIpSqVSOVsDAACAc1pZAR4R0draGkuWLIk5c+bE3LlzY82aNdHX1xdLly6NiIjFixfH9OnTo729PWpqauLKK68ctv6iiy6KiDhhHAAAAN7Lyg7wBQsWxOHDh2PlypXR1dUVs2bNii1btgx9MNuBAweisnJMf7UcAAAAxp2KoiiKs72Jd9Pb2xt1dXXR09MTtbW1Z3s7AAAAvMeNRYe6VQ0AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJRhXga9eujRkzZkRNTU00NjbGtm3bTjp3/fr1ccMNN8TEiRNj4sSJ0dzc/I7zAQAA4L2o7ADfuHFjtLa2RltbW+zYsSNmzpwZLS0tcejQoRHnb926NRYuXBgvvfRSdHZ2RkNDQ9x0003xxhtvnPbmAQAAYLyoKIqiKGdBY2NjXHvttfH4449HRMTg4GA0NDTEnXfeGcuXL3/X9QMDAzFx4sR4/PHHY/Hixad0zd7e3qirq4uenp6ora0tZ7sAAABQtrHo0LLugPf398f27dujubn5j09QWRnNzc3R2dl5Ss/x1ltvxdtvvx0XX3zxSeccO3Ysent7hz0AAABgPCsrwI8cORIDAwNRX18/bLy+vj66urpO6TnuvffemDZt2rCI/1Pt7e1RV1c39GhoaChnmwAAAHDOSf0U9NWrV8eGDRviueeei5qampPOW7FiRfT09Aw9Dh48mLhLAAAAOPMmlDN50qRJUVVVFd3d3cPGu7u7Y8qUKe+49pFHHonVq1fHT37yk7j66qvfcW6pVIpSqVTO1gAAAOCcVtYd8Orq6pg9e3Z0dHQMjQ0ODkZHR0c0NTWddN23vvWtePjhh2PLli0xZ86c0e8WAAAAxqmy7oBHRLS2tsaSJUtizpw5MXfu3FizZk309fXF0qVLIyJi8eLFMX369Ghvb4+IiH/8x3+MlStXxjPPPBMzZswY+l3xD3zgA/GBD3zgDL4UAAAAOHeVHeALFiyIw4cPx8qVK6OrqytmzZoVW7ZsGfpgtgMHDkRl5R9vrH/729+O/v7++Nu//dthz9PW1hZf+9rXTm/3AAAAME6U/T3gZ4PvAQcAACDTWf8ecAAAAGB0BDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJBDgAAAAkEOAAAACQQIADAABAAgEOAAAACQQ4AAAAJBDgAAAAkECAAwAAQAIBDgAAAAkEOAAAACQQ4AAAAJBAgAMAAEACAQ4AAAAJRhXga9eujRkzZkRNTU00NjbGtm3b3nH+D37wg7jiiiuipqYmrrrqqti8efOoNgsAAADjVdkBvnHjxmhtbY22trbYsWNHzJw5M1paWuLQoUMjzn/55Zdj4cKFceutt8bOnTtj/vz5MX/+/Hj11VdPe/MAAAAwXlQURVGUs6CxsTGuvfbaePzxxyMiYnBwMBoaGuLOO++M5cuXnzB/wYIF0dfXFz/+8Y+Hxv76r/86Zs2aFevWrTula/b29kZdXV309PREbW1tOdsFAACAso1Fh04oZ3J/f39s3749VqxYMTRWWVkZzc3N0dnZOeKazs7OaG1tHTbW0tISzz///Emvc+zYsTh27NjQzz09PRHx+/8DAAAAYKz9oT/LvGf9jsoK8CNHjsTAwEDU19cPG6+vr489e/aMuKarq2vE+V1dXSe9Tnt7ezz00EMnjDc0NJSzXQAAADgt//M//xN1dXVn5LnKCvAsK1asGHbX/M0334wPfvCDceDAgTP2wuFc09vbGw0NDXHw4EG/asF7lnPO+4FzzvuBc877QU9PT1xyySVx8cUXn7HnLCvAJ02aFFVVVdHd3T1svLu7O6ZMmTLimilTppQ1PyKiVCpFqVQ6Ybyurs4fcN7zamtrnXPe85xz3g+cc94PnHPeDyorz9y3d5f1TNXV1TF79uzo6OgYGhscHIyOjo5oamoacU1TU9Ow+RERL7744knnAwAAwHtR2W9Bb21tjSVLlsScOXNi7ty5sWbNmujr64ulS5dGRMTixYtj+vTp0d7eHhERd911V9x4443x6KOPxs033xwbNmyIn//85/Hkk0+e2VcCAAAA57CyA3zBggVx+PDhWLlyZXR1dcWsWbNiy5YtQx+0duDAgWG36K+77rp45pln4oEHHoj77rsv/uqv/iqef/75uPLKK0/5mqVSKdra2kZ8Wzq8VzjnvB8457wfOOe8HzjnvB+MxTkv+3vAAQAAgPKdud8mBwAAAE5KgAMAAEACAQ4AAAAJBDgAAAAkOGcCfO3atTFjxoyoqamJxsbG2LZt2zvO/8EPfhBXXHFF1NTUxFVXXRWbN29O2imMXjnnfP369XHDDTfExIkTY+LEidHc3Pyufy7gXFDu3+d/sGHDhqioqIj58+eP7QbhDCj3nL/55puxbNmymDp1apRKpbj88sv9twvnvHLP+Zo1a+LDH/5wnH/++dHQ0BD33HNP/O53v0vaLZTnpz/9acybNy+mTZsWFRUV8fzzz7/rmq1bt8bHP/7xKJVK8aEPfSiefvrpsq97TgT4xo0bo7W1Ndra2mLHjh0xc+bMaGlpiUOHDo04/+WXX46FCxfGrbfeGjt37oz58+fH/Pnz49VXX03eOZy6cs/51q1bY+HChfHSSy9FZ2dnNDQ0xE033RRvvPFG8s7h1JV7zv9g//798ZWvfCVuuOGGpJ3C6JV7zvv7++NTn/pU7N+/P5599tnYu3dvrF+/PqZPn568czh15Z7zZ555JpYvXx5tbW2xe/fueOqpp2Ljxo1x3333Je8cTk1fX1/MnDkz1q5de0rzf/nLX8bNN98cn/zkJ2PXrl1x9913x2233RYvvPBCeRcuzgFz584tli1bNvTzwMBAMW3atKK9vX3E+Z/73OeKm2++edhYY2Nj8fd///djuk84HeWe8z91/Pjx4sILLyy+973vjdUW4bSN5pwfP368uO6664rvfOc7xZIlS4rPfvazCTuF0Sv3nH/7298uLr300qK/vz9ri3Dayj3ny5YtK/7mb/5m2Fhra2tx/fXXj+k+4UyIiOK55557xzlf/epXi4997GPDxhYsWFC0tLSUda2zfge8v78/tm/fHs3NzUNjlZWV0dzcHJ2dnSOu6ezsHDY/IqKlpeWk8+FsG805/1NvvfVWvP3223HxxReP1TbhtIz2nH/961+PyZMnx6233pqxTTgtoznnP/rRj6KpqSmWLVsW9fX1ceWVV8aqVatiYGAga9tQltGc8+uuuy62b98+9Db1ffv2xebNm+PTn/50yp5hrJ2pBp1wJjc1GkeOHImBgYGor68fNl5fXx979uwZcU1XV9eI87u6usZsn3A6RnPO/9S9994b06ZNO+EPPpwrRnPOf/azn8VTTz0Vu3btStghnL7RnPN9+/bFf/zHf8TnP//52Lx5c7z++uvxpS99Kd5+++1oa2vL2DaUZTTn/JZbbokjR47EJz7xiSiKIo4fPx533HGHt6DznnGyBu3t7Y3f/va3cf7555/S85z1O+DAu1u9enVs2LAhnnvuuaipqTnb24Ez4ujRo7Fo0aJYv359TJo06WxvB8bM4OBgTJ48OZ588smYPXt2LFiwIO6///5Yt27d2d4anDFbt26NVatWxRNPPBE7duyIH/7wh7Fp06Z4+OGHz/bW4Jxy1u+AT5o0KaqqqqK7u3vYeHd3d0yZMmXENVOmTClrPpxtoznnf/DII4/E6tWr4yc/+UlcffXVY7lNOC3lnvNf/OIXsX///pg3b97Q2ODgYERETJgwIfbu3RuXXXbZ2G4ayjSav8+nTp0a5513XlRVVQ2NfeQjH4murq7o7++P6urqMd0zlGs05/zBBx+MRYsWxW233RYREVdddVX09fXF7bffHvfff39UVrrvx/h2sgatra095bvfEefAHfDq6uqYPXt2dHR0DI0NDg5GR0dHNDU1jbimqalp2PyIiBdffPGk8+FsG805j4j41re+FQ8//HBs2bIl5syZk7FVGLVyz/kVV1wRr7zySuzatWvo8ZnPfGbo00UbGhoytw+nZDR/n19//fXx+uuvD/0DU0TEa6+9FlOnThXfnJNGc87feuutEyL7D//o9PvPuILx7Yw1aHmfDzc2NmzYUJRKpeLpp58u/vu//7u4/fbbi4suuqjo6uoqiqIoFi1aVCxfvnxo/n/+538WEyZMKB555JFi9+7dRVtbW3HeeecVr7zyytl6CfCuyj3nq1evLqqrq4tnn322+PWvfz30OHr06Nl6CfCuyj3nf8qnoDMelHvODxw4UFx44YXFl7/85WLv3r3Fj3/842Ly5MnFN77xjbP1EuBdlXvO29raigsvvLD4t3/7t2Lfvn3Fv//7vxeXXXZZ8bnPfe5svQR4R0ePHi127txZ7Ny5s4iI4rHHHit27txZ/OpXvyqKoiiWL19eLFq0aGj+vn37igsuuKD4h3/4h2L37t3F2rVri6qqqmLLli1lXfecCPCiKIp/+Zd/KS655JKiurq6mDt3bvFf//VfQ//bjTfeWCxZsmTY/O9///vF5ZdfXlRXVxcf+9jHik2bNiXvGMpXzjn/4Ac/WETECY+2trb8jUMZyv37/P8nwBkvyj3nL7/8ctHY2FiUSqXi0ksvLb75zW8Wx48fT941lKecc/72228XX/va14rLLrusqKmpKRoaGoovfelLxf/+7//mbxxOwUsvvTTif2v/4VwvWbKkuPHGG09YM2vWrKK6urq49NJLi3/9138t+7oVReE9IQAAADDWzvrvgAMAAMD7gQAHAACABAIcAAAAEghwAAAASCDAAQAAIIEABwAAgAQCHAAAABIIcAAAAEggwAEAACCBAAcAAIAEAhwAAAASCHAAAABI8P8AhWws06D2oEcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import torch\n",
    "\n",
    "data_dict = {}\n",
    "\n",
    "# for data, target in tqdm(trainloader):\n",
    "#   if target.item() in data_dict:\n",
    "#     continue\n",
    "#   data_dict[target.item()] = data\n",
    "# #   break\n",
    "#   if len(data_dict) == 10:\n",
    "#     break\n",
    "# net = net.to('cpu')\n",
    "# net.eval()\n",
    "net.eval()\n",
    "for data, target in tqdm(iter(trainloader)):\n",
    "    if target.item() in data_dict:\n",
    "        continue\n",
    "    # data_dict[target.item()] = data\n",
    "    data =  data.to(device)\n",
    "    fig, ax = plt.subplots(facecolor='w', figsize=(12, 7))\n",
    "    labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n",
    "    k = target.item()\n",
    "    print(f\"The target label is: {labels[k]}\")\n",
    "    # break\n",
    "    spk_rec = forward_pass(net, data).detach().cpu().numpy()\n",
    "    data_dict[target.item()] = spk_rec\n",
    "    if len(data_dict) == 10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = 0\n",
    "for k, spk_rec in data_dict.items():\n",
    "    # print(spk_rec.shape)\n",
    "    # 创建一个空白图像，用于绘制动画的每一帧\n",
    "    fig.canvas.draw()\n",
    "    frame = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)\n",
    "    frame = frame.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n",
    "\n",
    "    # 创建一个可写的副本\n",
    "    frame_writable = frame.copy()\n",
    "\n",
    "    # 获取脉冲计数数据\n",
    "    spike_counts = spk_rec[:, idx]\n",
    "    df = pd.DataFrame(spike_counts)\n",
    "    df.to_csv(f'spike_count_{k}.csv', index=False)\n",
    "    # 创建颜色映射\n",
    "    cmap = plt.get_cmap('viridis')\n",
    "\n",
    "    # 创建VideoWriter对象\n",
    "    fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n",
    "    out = cv2.VideoWriter(f'spike_count_{k}.mp4', fourcc, 10.0, (frame.shape[1], frame.shape[0]))\n",
    "\n",
    "    # 绘制动画的每一帧\n",
    "    for t in range(len(spike_counts)):\n",
    "        # 清空图像\n",
    "        frame_writable.fill(0)\n",
    "\n",
    "        # 绘制条形图\n",
    "        for i, count in enumerate(spike_counts[t]):\n",
    "            color = cmap(i / len(labels))[:3]\n",
    "            color = list((np.array(color) * 255).astype(int))\n",
    "            # print(color)\n",
    "            color = [0, 255, 0]\n",
    "            cv2.rectangle(frame_writable, (i * 50, 0), ((i + 1) * 50, int(count * 100)),\n",
    "                        color, -1)\n",
    "            cv2.putText(frame_writable, labels[i], (i * 50 + 10, 120), cv2.FONT_HERSHEY_SIMPLEX,\n",
    "                        1, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "\n",
    "        # 将当前帧写入视频\n",
    "        out.write(frame_writable)\n",
    "\n",
    "    # 释放VideoWriter对象\n",
    "    out.release()"
   ]
  }
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